👉 Opposed computing, also known as adversarial computing or adversarial machine learning, is a concept where systems are designed to be resilient against deliberate attempts to mislead or manipulate them through carefully crafted inputs, known as adversarial examples. These examples are often imperceptible to humans but can cause the system to make incorrect predictions or decisions. This approach highlights the vulnerabilities in machine learning models and emphasizes the need for robustness and security measures to ensure that systems can reliably handle such attacks. By understanding and addressing these weaknesses, researchers and developers aim to create more secure and reliable AI systems.
